Conformal Selective Prediction with General Risk Control

• URL: http://arxiv.org/abs/2603.24704v1
• Date: Wed, 25 Mar 2026 18:29:23 GMT
• Title: Conformal Selective Prediction with General Risk Control
• Abstract summary: We propose a new framework for deriving such decisions for any trained model and any user-defined, bounded and continuously-valued risk.<n>SCoRE offers two types of guarantees on the risk among positive'' cases in which the system opts to trust the model.<n>We evaluate the proposed methods with simulations and demonstrate their efficacy through applications to error management in drug discovery, health risk prediction, and large language models.
• Score: 8.936337121607677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In deploying artificial intelligence (AI) models, selective prediction offers the option to abstain from making a prediction when uncertain about model quality. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is trusted. We propose Selective Conformal Risk control with E-values (SCoRE), a new framework for deriving such decisions for any trained model and any user-defined, bounded and continuously-valued risk. SCoRE offers two types of guarantees on the risk among ``positive'' cases in which the system opts to trust the model. Built upon conformal inference and hypothesis testing ideas, SCoRE first constructs a class of (generalized) e-values, which are non-negative random variables whose product with the unknown risk has expectation no greater than one. Such a property is ensured by data exchangeability without requiring any modeling assumptions. Passing these e-values on to hypothesis testing procedures, we yield the binary trust decisions with finite-sample error control. SCoRE avoids the need of uniform concentration, and can be readily extended to settings with distribution shifts. We evaluate the proposed methods with simulations and demonstrate their efficacy through applications to error management in drug discovery, health risk prediction, and large language models.

Related papers

• Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control [31.006941545235396]
  We study risk-averse decision making in which an agent selects actions while being uncertain about the true system state.<n>We characterize the optimal policy under known distributions, and show that it reduces to a prediction set-based solution for the CVaR.<n>For unknown distributions, we develop a data-driven calibration strategy, based on a synthetic model for the likelihood and held-out calibration data.
  arXiv  Detail & Related papers  (2026-08-28T10:45:23Z)
• Entropy Alone is Insufficient for Safe Selective Prediction in LLMs [20.664633053172327]
  Selective prediction systems can mitigate harms resulting from language model hallucinations by abstaining from answering in high-risk cases.<n>Uncertainty quantification techniques are often employed to identify such cases, but are rarely evaluated in the context of the wider selective prediction policy.<n>We identify a model-dependent failure mode of entropy-based uncertainty methods that leads to unreliable abstention behaviour, and address it by combining entropy scores with a correctness probe signal.
  arXiv  Detail & Related papers  (2026-03-22T11:27:13Z)
• LEC: Linear Expectation Constraints for False-Discovery Control in Selective Prediction and Routing Systems [95.35293543918762]
  Large language models (LLMs) often generate unreliable answers, while uncertainty methods fail to fully distinguish correct from incorrect predictions.<n>We address this issue through the lens of false discovery rate (FDR) control, ensuring that among all accepted predictions, the proportion of errors does not exceed a target risk level.<n>We propose LEC, which reinterprets selective prediction as a constrained decision problem by enforcing a Linear Expectation Constraint.
  arXiv  Detail & Related papers  (2025-12-01T11:27:09Z)
• Trusted Uncertainty in Large Language Models: A Unified Framework for Confidence Calibration and Risk-Controlled Refusal [31.458406135473805]
  We present UniCR, a unified framework that turns heterogeneous uncertainty evidence into a calibrated probability of correctness.<n>UniCR learns a lightweight calibration head with temperature scaling and proper scoring.<n>Experiments on short-form QA, code generation with execution tests, and retrieval-augmented long-form QA show consistent improvements in calibration metrics.
  arXiv  Detail & Related papers  (2025-09-01T13:14:58Z)
• Calibrated Prediction Set in Fault Detection with Risk Guarantees via Significance Tests [3.500936878570599]
  This paper proposes a novel fault detection method that integrates significance testing with the conformal prediction framework to provide formal risk guarantees.<n>The proposed method consistently achieves an empirical coverage rate at or above the nominal level ($1-alpha$)<n>The results reveal a controllable trade-off between the user-defined risk level ($alpha$) and efficiency, where higher risk tolerance leads to smaller average prediction set sizes.
  arXiv  Detail & Related papers  (2025-08-02T05:49:02Z)
• Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity [18.09405926516524]
  Calibrating machine learning models to achieve risk control is crucial to ensure reliable decision-making.<n>We introduce Performative Risk Control, a framework to calibrate models to achieve risk control under performativity.
  arXiv  Detail & Related papers  (2025-05-30T00:59:25Z)
• SConU: Selective Conformal Uncertainty in Large Language Models [59.25881667640868]
  We propose a novel approach termed Selective Conformal Uncertainty (SConU)<n>We develop two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level.<n>Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions.
  arXiv  Detail & Related papers  (2025-04-19T03:01:45Z)
• Calibrating AI Models for Wireless Communications via Conformal Prediction [55.47458839587949]
  Conformal prediction is applied for the first time to the design of AI for communication systems. This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees.
  arXiv  Detail & Related papers  (2022-12-15T12:52:23Z)
• Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval [51.83967175585896]
  UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction.
  arXiv  Detail & Related papers  (2022-10-24T17:53:20Z)
• Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty [68.00748155945047]
  Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
  arXiv  Detail & Related papers  (2022-10-12T07:57:03Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.